Methods, Systems and Computer Program Products for Diagnosing Conditions Using Unique Codes Generated from a Multidimensional Image of a Sample

ABSTRACT

Methods of providing a diagnosis using a digital code associated with an image are provided including collecting a multidimensional image, the multidimensional image having at least two dimensions; extracting a two dimensional subset of the multidimensional image; reducing the multidimensional image to a first code that is unique to the multidimensional image based on the extracted two dimensional image; comparing the first unique code associated with the subject to a library of reference codes, each of the reference codes in the library of reference codes being indicative of a class of objects; determining if the subject associated with the first unique code falls into at least one of the classes of objects associated with the reference codes based on a result of the comparison; and formulating a diagnostic decision based on the whether the first unique code associated with the subject falls into at least one of the classes associated with the reference code. Related systems and computer program products are also provided herein.

CLAIM OF PRIORITY

The present application is a continuation of U.S. patent applicationSer. No. 12/953,868, filed Nov. 24, 2010 (now U.S. Pat. No. ______),which is a continuation-in-part of U.S. patent application Ser. No.12/624,937, filed Nov. 24, 2009 (U.S. Pat. No. 8,687,856), which claimspriority to U.S. Provisional Application No. 61/118,087, filed Nov. 26,2008, and claims priority to U.S. Provisional Application No.61/263,991, filed Nov. 24, 2009, the disclosures of which are herebyincorporated herein by reference as if set forth in their entirety.

FIELD

The present inventive concept relates generally to optical coherencetomography (OCT) and, more particularly, to biometric identificationsystems that use OCT.

BACKGROUND

The field of biometrics has rapidly evolved in recent years as the needfor fast, accurate personal identification has increased. Motivatedprimarily by national security concerns, law enforcement agencies haveexpanded well beyond traditional fingerprint identification to, forexample, classifiers based on facial topology, vocal patterns, irisstructure, and retinal vessel landmarks.

A very good biometric identifier may be one that is measured quickly andeasily and is not easily susceptible to cosmetic modification orfalsification. This coupled with the work of Daugman and Downing (Proc.R. Soc. Lond. B 268, 1737-1740 (2001)) has led to the development ofmultiple commercially available iris recognition systems.

Using the iris in recognition systems is currently becoming morecommonplace. However, iris recognition is a complete paradigm shift fromtraditional fingerprint recognition systems and, therefore, can becostly to implement and to reconstruct data bases of existingidentification files. Furthermore, it may be possible to thwart irisidentification using, for example, a patterned contact lens.

U.S. Pat. No. 5,751,835 to Topping et al. discusses the use of thetissue of the fingernail bed as a unique identifier. The tissue under afingernail is composed of characteristic ridges that form duringgestation and are unique to each individual. While the ridges mayincrease in size as the individual grows, the spacing between the ridgesremains consistent over time.

Optical coherence tomography (OCT) is a noninvasive imaging techniquethat provides microscopic sectioning of biological samples. By measuringsingly backscattered light as a function of depth, OCT fills a valuableniche in imaging of tissue structure, providing subsurface imaging withhigh spatial resolution (˜2.0-10.0 μm) in three dimensions and highsensitivity (>110 dB) in vivo with no contact needed between the probeand the tissue.

In biological and biomedical imaging applications, OCT allows formicrometer-scale, non-invasive imaging in transparent, translucent,and/or highly-scattering biological tissues. The depth rangingcapability of OCT is generally based on low-coherence interferometry, inwhich light from a broadband source is split between illuminating thesample of interest and a reference path. The interference pattern oflight reflected or backscattered from the sample and light from thereference delay contains information about the location and scatteringamplitude of the scatterers in the sample.

In time-domain OCT (TDOCT), this information is typically extracted byscanning the reference path delay and detecting the resultinginterferogram pattern as a function of that delay. The envelope of theinterferogram pattern thus detected represents a map of the reflectivityof the sample versus depth, generally called an A-scan, with depthresolution given by the coherence length of the source. In OCT systems,multiple A-scans are typically acquired while the sample beam is scannedlaterally across the tissue surface, building up a two-dimensional mapof reflectivity versus depth and lateral extent typically called aB-scan. The lateral resolution of the B-scan is approximated by theconfocal resolving power of the sample arm optical system, which isusually given by the size of the focused optical spot in the tissue. Thetime-domain approach used in conventional OCT has been successful insupporting biological and medical applications, and numerous in vivohuman clinical trials of OCT reported to date have utilized thisapproach.

An alternate approach to data collection in OCT has been shown to havesignificant advantages both in reduced system complexity and inincreased signal-to-noise ratio (SNR). This approach involves acquiringthe interferometric signal generated by mixing sample light withreference light at a: fixed group delay in the wavelength or frequencydomain and processing the Fourier transform of this spectralinterferogram from a wavenumber to a spatial domain. Two distinctmethods have been developed which use this Fourier domain OCT (FDOCT)approach. The first, generally termed Spectral-domain orspectrometer-based OCT (SDOCT), uses a broadband light source andachieves spectral discrimination with a dispersive spectrometer in thedetector arm. The second, generally termed swept-source OCT (SSOCT) oroptical frequency-domain imaging (OFDI), time-encodes wavenumber byrapidly tuning a narrowband source through a broad optical bandwidth.Both of these techniques may allow for a dramatic improvement in SNR ofup to 15.0-20.0 dB over time-domain OCT, because they detect all of thebackscattered power from the entire relevant sample depth in eachmeasurement interval. This is in contrast to previous-generationtime-domain OCT, where destructive interference is typically used toisolate the interferometric signal from only one depth at a time as thereference delay is scanned.

SUMMARY

Some embodiments of the present inventive concept provide methods ofproviding a diagnosis using a digital code associated with an image, themethod including collecting a multidimensional image, themultidimensional image having at least two dimensions; extracting a twodimensional subset of the multidimensional image; reducing themultidimensional image to a first code that is unique to themultidimensional image based on the extracted two dimensional image;comparing the first unique code associated with the subject to a libraryof reference codes, each of the reference codes in the library ofreference codes being indicative of a class of objects; determining ifthe subject associated with the first unique code falls into at leastone of the classes of objects associated with the reference codes basedon a result of the comparison; and formulating a diagnostic decisionbased on the whether the first unique code associated with the subjectfalls into at least one of the classes associated with the referencecode.

In further embodiments of the present inventive concept, determining ifthe subject associated with the first unique reference code falls intoat least one of the classes may further include determining if thesubject associated with the first unique reference code has changedclasses over time and formulating the diagnostic decision may includeformulating the diagnostic decision based on the change of class overtime.

In still further embodiments, determining if the subject associated withthe first unique code falls into at least one of the classes of objectsmay include determining that the subject associated with the firstunique code falls into at least two of the classes. The method mayfurther include applying additional processing to determine which of theat least two classes more accurately identifies the subject associatedwith the first unique code.

In some embodiments, the classes associated with the reference code mayidentify at least one of a physical state and a physical object. Thesubject may include at least one of a fingernail bed, a fingerprint, aniris, a cornea, any human tissue and physical object.

In further embodiments of the present inventive concept, reducing mayfurther include reducing the two dimensional subset to the first uniquecode based on a defined set of structural or functional informationcontained with the image. The method may further include storing thefirst unique code; and comparing the first unique code to a secondunique code to establish a degree of equivalence of the first and secondcodes.

In still further embodiments of the present inventive concept, themultidimensional image may include at least one of a volumetric imagerepresentative of time invariant information in a sample, slowlytime-variant information in a sample, or time variant structural orfunctional information in a sample.

In some embodiments of the present inventive concept, reducing the twodimensional subset to the first unique code may include manipulating themultidimensional data to extract a region of interest; extracting thestructural or the functional information using a filter; translating thefiltered information into the first unique code representative of theinformation content contained in the multidimensional data; manipulatingthe first unique code to be compared to other codes; comparing two ormore codes to uniquely identify each code; and assigning a uniqueidentifier to each code.

In further embodiments of the present inventive concept, extracting mayinclude extracting using a filter configured to extract the structuralor functional information. The filter may include at least one of aGabor filter, a complex filter that consists of real and imaginaryparts, a complex filter that consists of a spatial frequency componentand a Gaussian window component and a complex filter that has at leastthree unique degrees of freedom including amplitude, at least onespatial frequency, and at least one Gaussian window standard deviation.

In still further embodiments of the present inventive concept, thefilter may be configured to operate on a two dimensional subset using atleast one of a convolution in the native domain or a multiplication ofthe Fourier Transforms of the filter and two dimensional subset andmultiple filter scales in which one or more filter degrees of freedomare changed before combination with the image subset.

In some embodiments of the present inventive concept, the unique codemay be obtained from complex data comprising at least one of a complexrepresentation of the filter combined with the image subset in which thecomplex result is treated as a vector in the complex plane and a methodin which the angle of the vector in the complex plane is determined anda code representative of the quadrant in the complex plane containingthe vector angle is generated.

In further embodiments of the present inventive concept, the unique codemay be binary such that each pixel of information is represented by oneof two states.

In still further embodiments of the present inventive concept, theunique code may have a base greater than 2.

In still further embodiments of the present inventive concept, theunique code may be represented as a one or two dimensional barcodeconfigured to be read by a generic commercial barcode readingtechnology.

In some embodiments of the present inventive concept, comparing mayinclude comparing two or more unique codes using cross-correlation orother relational comparison.

In further embodiments of the present inventive concept, the relationalcomparison may be an XOR operation. The comparison result may be appliedto find a Hamming distance. The Hamming distance may be used to validatea strength of the comparison against a database of other codes.

In still further embodiments of the present inventive concept, theextracting, translating, manipulating, comparing and assigning steps maybe repeated to construct a database of codes. A unique identifierthreshold may be defined based on sensitivity analysis of the codes inthe database. The method may further include determining if a calculatedcode is unique or present in the database by comparing the uniqueidentifier threshold to the Hamming distance between the calculated codeand the codes in the database.

In some embodiments of the present inventive concept, the filter mayinclude any image processing system in which information content isemphasized or extracted from a depth-dependent image.

In further embodiments of the present inventive concept, the filter maybe at least one of a speckle tracking algorithm and a texture-basedimage analysis algorithm.

Still further embodiments of the present inventive concept providemethods of providing a diagnosis based on a digital code in an opticalcoherence tomography imaging system, the method including acquiringinterferometric cross-correlation data representative ofmultidimensional information unique to a sample, the multidimensionalinformation including one, two, or three spatial dimensions plus zero,one or two time dimensions; processing the multidimensionalinterferometric cross-correlation data into one or more images processedto represent one or more of time invariant information about the sample,slowly time variant information about the sample, or time variantstructural or functional information about the sample; and selecting asubset of the multidimensional data; reducing the selected subset ofdata to a two dimensional subset of data; performing one or more spatialor temporal frequency transforms of the two dimensional subsets toderive a unique representation of the sample; reducing the transforminto a unique digital code associated with the sample; comparing theunique digital code associated with the sample to a library of referencecodes, each of the reference codes in the library of reference codesbeing indicative of a class of objects; determining if the sampleassociated with the unique digital code falls into at least one of theclasses of objects associated with the reference codes based on a resultof the comparison; and formulating a diagnostic decision based on thewhether the unique digital code associated with the sample falls into atleast one of the classes associated with the reference code.

In some embodiments of the present inventive concept the opticalcoherence tomography system may include an optical source; an opticalsplitter configured to separate a reference optical signal from a sampleoptical signal; and an optical detector configured to detect aninterferometric cross-correlation between the reference optical signaland the sample optical signal.

In further embodiments of the present inventive concept, the uniquedigital code may include a first unique digital code. The method mayfurther include storing the digital code; comparing the first uniquedigital code to a second unique digital code of the sample acquired froma second position within the sample, of the same sample acquired at adifferent time and/or of a different sample; and establishing a degreeof equivalence between the first and second unique digital codes.

Still further embodiments of the present inventive concept providemethods of providing a diagnosis using a digital code using a Fourierdomain optical coherence tomography imaging system, the method includingacquiring frequency-dependent interferometric cross-correlation datarepresentative of multidimensional information unique to a sample, themultidimensional information including zero or one frequency dimensions,one, two, or three spatial dimensions and zero, one, or two timedimensions; processing the multidimensional interferometriccross-correlation data into one or more images processed to representone or more of time invariant information about the sample, slowly timevariant information about the sample, or time variant structural orfunctional information about the sample; selecting a subset of themultidimensional data; reducing the subset of data to a two dimensionalsubset of data; performing one or more spatial or temporal frequencytransforms of the two dimensional subsets to derive a uniquerepresentation of the sample; reducing the transform into a uniquedigital code that provides a unique signature of the multidimensionaldata; comparing the unique digital code associated with the sample to alibrary of reference codes, each of the reference codes in the libraryof reference codes being indicative of a class of objects; determiningif the sample associated with the unique digital code falls into atleast one of the classes of objects associated with the reference codesbased on a result of the comparison; and formulating a diagnosticdecision based on the whether the unique digital code associated withthe sample falls into at least one of the classes associated with thereference code.

In some embodiments of the present inventive concept, the Fourier domainoptical coherence tomography imaging system may include an opticalsource; an optical splitter configured to separate a reference opticalsignal from a sample optical signal; and an optical detector configuredto detect a frequency-dependent interferometric cross-correlationbetween the reference signal and the sample signal.

In further embodiments of the present inventive concept, the uniquedigital code may be a first unique digital code and the method mayfurther include storing the unique digital code; comparing the uniquedigital code to a second unique digital code of the same sample acquiredfrom a separate position within the sample, of the sample acquired at aseparate time, of a different sample; and establishing a degree ofequivalence between the first and second unique digital codes.

In still further embodiments of the present inventive concept,processing of frequency-dependent interferometric cross-correlation datamay include obtaining a Fourier transformation of thefrequency-dependent dimension to provide a spatial dimension.

In some embodiments of the present inventive concept, the image may be avolumetric image of a fingernail of a subject, the volumetric image mayinclude a series of one-dimensional lines that provide information onscattering from the fingernail and fingernail bed as a function ofdepth, a series of lines optically contiguous may be arrayed in atwo-dimensional frame that represents a cross section of the fingernailperpendicular to an axis of the finger, and the method may furtherinclude acquiring a series of frames to produce a volume.

In further embodiments of the present inventive concept, the method mayfurther include segmenting a nailbed from within the volumetric image ofthe fingernail using an automated or manual segmentation technique; andaveraging one or more frames in order to produce an average-valued imageof multiple cross-sectional locations of the nailbed or to improve thesignal-to-noise ratio of the nailbed image along one cross-section.

In still further embodiments of the present inventive concept, themethod may further include processing a digital code from thecross-sectional image of segmented nailbed region of at least one frame.Processing the multidimensional interferometric cross-correlation datamay include processing an intensity projection from two or more framesof the segmented nailbed, the method further comprising processing adigital code from the intensity projection.

In some embodiments of the present inventive concept, the image may be avolumetric image of an iris of an eye of a subject, the volumetric imagemay include a series of one-dimensional lines that provide informationon scattering from the iris as a function of depth, a series of linesoptically contiguous may be arrayed in a two-dimensional frame thatrepresents a cross section of the iris perpendicular to an axis of theeye, and the method may further include acquiring a series of frames toproduce a volume.

In further embodiments of the present inventive concept, the method mayfurther include constructing the volumetric image from a series ofconcentric circular scans approximately centered on a pupil of the eye.

In still further embodiments of the present inventive concept, themethod may further include segmenting one or more layers of the irisfrom within the volumetric image of the iris using an automated ormanual segmentation technique; and averaging one or more frames in orderto produce an average-valued image of multiple cross-sectional locationsof the iris or to improve the signal-to-noise ratio of the iris image atcross section.

In some embodiments of the present inventive concept, the method furtherincludes processing a digital code from the cross-sectional image ofsegmented iris region of at least one frame.

In further embodiments of the present inventive concept, the method mayfurther include processing an intensity projection from two or moreframes of the segmented iris; and processing a digital code from theintensity projection.

Although embodiments of the present inventive concept are primarilydiscussed above with respect to method embodiments, systems and computerprogram products are also discussed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a raster scanning FDOCT systemfor fingernail bed imaging according to some embodiments of the presentinventive concept.

FIG. 2 is a block diagram of a full-field FDOCT system for fingernailbed imaging according to some embodiments of the present inventiveconcept.

FIG. 3 is a block diagram of a raster scanning FDOCT system including adigital capture system for fingernail bed imaging according to someembodiments of the present inventive concept.

FIG. 4 is a block diagram of a dual, raster scanning FDOCT system forcombined fingernail bed and fingerprint imaging according to someembodiments of the present inventive concept.

FIG. 5 is a block diagram of a dual, full-field FDOCT system forcombined fingernail bed and fingerprint imaging according to someembodiments of the present inventive concept.

FIG. 6 is a block diagram of a raster scanning FDOCT system for irisimaging according to some embodiments of the present inventive concept.

FIG. 7 is a block diagram of a full-field FDOCT system for iris imagingaccording to some embodiments of the present inventive concept.

FIG. 8 is a block diagram of a raster scanning FDOCT system withcombined digital capture system for iris imaging according to someembodiments of the present inventive concept.

FIG. 9 is a block diagram illustrating representative reference armconfigurations according to some embodiments of the present inventiveconcept.

FIG. 10 is a block diagram illustrating representative FDOCT detectionmethods according to some embodiments of the present inventive concept.

FIG. 11 are images and graphs illustrating slice planes through a 3Dvolume in the fingernail bed according to some embodiments of theinventive concept.

FIGS. 12A, B and C are graphs and images illustrating generation andevolution of the Gabor filter according to some embodiments of thepresent inventive concept.

FIG. 13 is a diagram illustrating decision code generation according tosome embodiments of the present inventive concept.

FIG. 14 is a diagram illustrating decision code processing and analysisaccording to some embodiments of the present inventive concept.

FIG. 15 is a diagram illustrating unique code generation from 1 sliceplane through a 3D volume of fingernail bed data according to someembodiments of the present inventive concept.

FIG. 16 is series of images illustrating OCT slices separated in timethrough finger 9 and the subsets used for unique, matching codegeneration according to some embodiments of the present inventiveconcept.

FIG. 17 is a series of images illustrating OCT slices from fingers 9 and4 and the subsets used for unique, non-matching code generationaccording to some embodiments of the present inventive concept.

FIG. 18 is a series of images illustrating OCT slices from fingers 3 and4 and the subsets used for unique, non-matching code generationaccording to some embodiments of the present inventive concept.

FIG. 19 is a flowchart illustrating processing steps in high-throughputbiometric identification in accordance with some embodiments of thepresent inventive concept.

FIG. 20 a diagram illustrating a series of slice planes through a 3Dvolume in the iris and representative OCT images from said planesaccording to some embodiments of the inventive concept.

FIG. 21 are diagrams of unique code generation from 1 slice through a 3Dvolume of iris data according to some embodiments of the inventiveconcept.

FIG. 22 is a block diagram of a data processing system suitable for usein some embodiments of the present inventive concept.

FIG. 23 is a more detailed block diagram of a system according to someembodiments of the present inventive concept.

FIG. 24 is a block diagram illustrating processing of N-dimensional datain accordance with some embodiments of the present inventive concept.

FIG. 25 is a block diagram illustrating processing of N-dimensional datain accordance with some embodiments of the present inventive concept.

FIG. 26 is a block diagram illustrating processing of N-dimensional datain accordance with some embodiments of the present inventive concept.

FIG. 27 is a block diagram illustrating generation of reference codes inaccordance with some embodiments of the present inventive concept.

FIG. 28 is a diagram illustrating decision code processing and analysisin accordance with some embodiments of the present inventive concept.

FIG. 29 a diagram illustrating decision code processing and analysis inaccordance with some embodiments of the present inventive concept.

FIG. 30 is a flowchart illustrating processing steps in accordance withsome embodiments of the present inventive concept.

FIG. 31 are images and graphs illustrating slice planes through a 3Dvolume in the fingerprint according to some embodiments of the presentinventive concept.

FIG. 32 is a diagram illustrating unique code generation from 1 sliceplane through a 3D volume of fingerprint data according to someembodiments of the present inventive concept.

FIG. 33 are images and graphs illustrating slice planes through a 3Dvolume in the retina according to some embodiments of the presentinventive concept.

FIG. 34 is a diagram illustrating unique code generation from 1 sliceplane through a 3D volume of retina data according to some embodimentsof the present inventive concept.

FIG. 35 is a diagram illustrating unique code generation from 1 sliceplane through a 3D volume of retina data according to some embodimentsof the present inventive concept.

FIG. 36 is a diagram illustrating unique code generation from 1 sliceplane through a 3D volume of retina vessels according to someembodiments of the present inventive concept.

FIG. 37 are images and graphs illustrating slice planes through a 3Dvolume in the retina vessels according to some embodiments of thepresent inventive concept.

DETAILED DESCRIPTION

The inventive concept now will be described more fully hereinafter withreference to the accompanying drawings, in which illustrativeembodiments of the inventive concept are shown. This inventive conceptmay, however, be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the inventive concept tothose skilled in the art. Like numbers refer to like elementsthroughout. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the inventiveconcept. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this inventive concept belongs. Itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andthis specification and will not be interpreted in an idealized or overlyformal sense unless expressly so defined herein.

As used herein, “time invariant” refers to a property that does notchange over a time period of interest. For example, the structure of theiris in a normal healthy individual. “Slowly time variant” refers to aproperty that does not vary measurably within one measurementacquisition, but may change between measurements. For example, thecourse of blood vessels in a patient with diabetic retinopathy. “Timevariant” refers to a property that is measurably changing during thecourse of one measurement event. For example, the pulsatility of anocular artery. “Structural information” refers to morphologicalinformation arising from a combination of scattering and absorption in atissue, without reference to, for example, spectroscopic or phaseinformation. “Functional information” refers to information arising outof a physiological or metabolic condition of a sample, for example,wavelength dependent absorption or scattering (spectroscopic),birefringence (polarization sensitivity), or phase or flow information(e.g. Doppler).

“En face” refers to a plane parallel to the plane of the surface of thetissue being imaged. “Intensity projection” refers to one of anyprocessing techniques known by those having skill in the art oranticipated to create an en face view of a volumetric image set. It willbe further understood that as used herein transformation of an opticalinformation set from frequency to time is equivalent to a transformationbetween frequency and space.

As is understood by those having skill in the art, a biometricidentification system typically uses a unique personal attribute thatcan be measured with high sensitivity and specificity. To obtain thebest results, this attribute should be an attribute that cannot beeasily modified or falsified cosmetically and yet is quickly and easilymeasured via non-invasive means. A secondary measurement may be acquiredto re-enforce the primary measurement or to correlate with traditionalmeasurement systems. For example, the primary attribute may be a humanfingernail bed as processed in an FDOCT imaging system, and thesecondary attribute may be an image of the fingerprint. The image of thesecondary attribute may be an analog or digital photograph, or it may bean en face image derived from an OCT image set. Although embodiments ofthe present inventive concept are discussed herein with respect to thehuman fingernail bed, embodiments of the present inventive concept arenot limited to this attribute. For example, the iris may also be used asthe attribute without departing from the scope of the present inventiveconcept. The attribute in accordance with some embodiments of thepresent inventive concept may be quickly and easily measured usingFourier Domain Optical Coherence Tomography (FDOCT) as will be discussedfurther herein with respect to FIGS. 1 through 23.

In particular, some embodiments of the present inventive conceptdepth-section the tissue below the nail and generate unique 2-Dtopographical maps of depth as a function of lateral position at eachspatial location across the nail using optical coherence tomography(OCT).

In particular, some embodiments of the present inventive concept discussa novel, non-invasive optical biometric classifier. Some embodimentsdiscussed herein utilize optical coherence tomography to resolve tissuemicrostructure in the human fingernail bed. However, it will beunderstood that the concepts can be applied using other depth-resolvedimaging techniques, such as scanning confocal microscopy, withoutdeparting from the scope of the present inventive concept.

In some embodiments of the present inventive concept, an FDOCT system isused to obtain a volumetric image of the target tissue. Light from anoptical source is directed via an optical splitter through a referenceand sample arm path, reflected off of reference reflector in thereference arm and backscattered from the tissue in the sample arm. Thereference and sample signals are mixed in an optical coupler and themixed light is directed to an optical detector. Interferometriccross-correlation terms are sampled in the frequency domain andprocessed using methods known to those skilled in the art to produce anarray of depth-dependent data at one spatially unique location on thesample; this array of depth resolved data is known as an A-scan.

An array of A-Scans acquired as a beam scans a direction across thesample and forms a two-dimensional cross-sectional image of a slice ofthe tissue; this cross-sectional image is known as a B-Scan. A B-Scanmay be useful for obtaining time-invariant or time slowly-variant imagesof a sample. A collection of multiple B-Scans acquired across the sampledefines an OCT volume.

In some embodiments of the present inventive concept, an array ofA-Scans acquired as a function of time at one spatial location is knownas an M-Scan. An M-scan may be useful for obtaining time-variant imagesof a sample.

In some embodiments of the present inventive concept, the OCT image maybe obtained by scanning the sample light across the sample and at eachspatially unique point to acquire a spectral interferogram whoseintensity is a function of the depth-dependent back-scattering intensityof the sample.

In some embodiments of the present inventive concept, full-field OCTimaging may be provided using a large collimated spot on the sample anda narrow instantaneous linewidth from the source. This can beaccomplished using, for example, a swept laser source or asuperluminescent diode with a tunable filter. In these embodiments ofthe present inventive concept, spectral images may be acquired in aplane parallel to the surface of the tissue, which are then processedusing FDOCT processing techniques known to those having skill in theart.

Projections through volumetric data sets can be used to decrease thedimensionality of the image data and provide a unique representation ofthe two dimensional (2D) structure appropriate to the attribute ofinterest. A projection created by processing the image intensity alongthe depth axis of a three dimensional (3D) volume produces an en faceimage of the volumetric data. This en face image can then be correlatedwith other OCT-generated en face images or photographs acquired throughother imaging modalities, for example, standard flash photography, totrack motion between subsequent volumes or to align the structure in thevolume data for longitudinal analysis.

In some embodiments, projecting through the data may be accomplishedusing the average intensity projection, also known as the volumeintensity projection, in which the signal intensity along each A-scan isaveraged, collapsing each depth line to a single point representative ofthe average intensity along each depth line. The 2D en face image iscomposed of all points generated from the processing above.

In further embodiments of the present inventive concept, generating anintensity projection is the maximum intensity projection, in which themaximum value of each A-Scan is calculated and stored, collapsing eachdepth line to a single point containing the maximum intensity valuealong each depth line. The 2D en face image is composed of all pointsgenerated from the processing above.

In some embodiments of the present inventive concept, generating anintensity projection is a histogram-based method in which the histogramof the intensity of each A-Scan is calculated and the maximum value ofthe histogram is stored, collapsing each depth line to a single pointcontaining the maximum value of the histogram for each depth line. The2D en face image is composed of all points generated from the processingabove.

In some embodiments of the present inventive concept, generating anintensity projection is a histogram-based method in which the histogramof the intensity of each A-Scan is calculated and the centroid or centerof mass of the histogram is stored, collapsing each depth line to asingle point containing the center of mass of the histogram for eachdepth line. The 2D en face image is composed of all points generatedfrom the processing above.

Although particular methods for calculating the intensity projectionhave been discussed above, embodiments of the present inventive conceptare not limited to this configuration. For example, methods may be usedto calculate the intensity projection based on standard algebraicoperations. The intensity projections stated may be used along axes notparallel to the plane of the tissue surface to create projectionsthrough the 3D volume that are not en face, but do create an alternate2D representation of the 3D volume data.

Referring first to FIG. 1, a block diagram illustrating a rasterscanning FDOCT system for imaging in accordance with some embodiments ofthe present inventive concept will be discussed. As illustrated in FIG.1, the FDOCT system includes a low-coherence source (LCS) 100, anoptical coupler (OC) 110, a reference arm (RA) 120, a sample arm (SA)130, a computer system (CS) 140, a detector (D) 150 and a sample 160.The LCS 100 may be, for example, a broadband superluminescent diodehaving a center wavelength in the near-infrared (IR) or IR range withsufficient bandwidth to have less than about 20 μm axial and lateralresolution in tissue. Light from the diode is passed through the opticalcoupler 110 to a fixed reference arm 120 discusses below with respect toFIGS. 9 and 21 and the sample arm 130 that raster scans the beam acrossthe sample 160. Reference and sample arm light are mixed in the opticalcoupler 110 and detected in parallel by the detector 150 using a onedimensional (1D) array detector, such as the SDOCT detector 151 of FIG.10.

In some embodiments, a tunable, or swept source may be used in place ofthe LCS 100, and the spectral interferogram may be collected seriallywith one or more point detectors. Once the spectral interferogram isacquired and resampled into a wavenumber array, the Fourier processingalgorithms for the SDOCT and the SSOCT images are similar.

Referring again to FIG. 1, the computer system 140 may be used toprocess and analyze the data. 2D slice scans or 3D volume scans may beused to acquire depth slices through the fingernail bed. For manyembodiments of the present inventive concept discussed herein, multipleA-Scan one dimensional (1D) depth lines or B-Scan 2D depth slices may beacquired and averaged as a function of time to improve thesignal-to-noise ratio of the acquired data.

Referring now to FIG. 2, a block diagram of a full-field FDOCT systemfor imaging in accordance with some embodiments of the present inventiveconcept will be discussed. Like elements refer to like referencenumerals throughout the specification. Accordingly, the LCS 100, theoptical coupler 110, the reference arm 120, the computer system 140, andthe sample 160 are similar to those discussed above with respect toFIG. 1. However, in embodiments illustrated in FIG. 2, a scanning fiberFabry-Perot (FFP) filter is used to rapidly sweep through the wavelengthoutput range. Light from the filter is passed through an optical coupler110 to a fixed reference arm 120 and a sample arm 200 that expands andcollimates the beam incident on a large area of the sample. Referenceand sample arm light are mixed in the optical coupler 110 and detectedusing 2-D, full-field array detector 151, such as the FFOCT detector 153of FIG. 10.

Referring now to FIG. 3, a block diagram of a raster scanning FDOCTsystem with a digital image capture capability according to someembodiments of the present inventive concept will be discussed. Asdiscussed above, like elements refer to like elements throughout.Accordingly the LCS 100, the optical coupler 110, the reference arm 120,the sample arm 130, the computer system 140, the detector 150 and thesample 160 are similar to those discussed above with respect to FIG. 1.However, as illustrated in FIG. 3, the system of FIG. 3 includes adigital capture system 300. The digital capture system 300 may be, forexample, a high speed video camera or a high resolution still camera.The digital capture system 300 illustrated in FIG. 3 may be used tocapture the fingerprint 161 of the sample while the FDOCT systemcaptures the fingernail bed data. The digitally captured image may beused in standard digital fingerprint identification databases to appendthe unique code to existing identification information.

Referring now to FIG. 4, a block diagram of a dual raster scanning FDOCTsystem according to some embodiments of the present inventive conceptwill be discussed. As discussed above, like elements refer to likeelements throughout. Accordingly the LCS 100, the optical coupler 110,the reference arm 120, the computer system 140, the detector 150 and thesample 160 are similar to those discussed above with respect to FIG. 1.However, the FDOCT system of FIG. 4 includes two samples arms 130 and400. The FDOCT sample arm 400 is used to raster scan the fingerprint 161at the same time as the fingernail bed 160 is raster scanned by 130. Thevolumetric FDOCT data may be processed to retrieve an en face image ofthe fingerprint, which in turn may be used in standard digitalfingerprint identification databases to append the unique code toexisting identification information. Embodiments of the presentinventive concept are not limited to the configuration of FIG. 4. Forexample, the system may be implemented using multiple reference armtopologies illustrated in FIG. 9.

Referring now to FIG. 9, various reference arm configurations inaccordance with some embodiments of the present inventive concept willbe discussed. As illustrated in FIG. 9, element 121 is a fixed pathlength reference arm. As further illustrated in FIG. 9, element 122 is aswitched reference arm that alternates between two unique reference armpositions, allowing serial acquisition of the signal from the two uniquesample arms. Still further illustrates in FIG. 9, element 123 is astatic reference arm in which the reference arm light is split betweentwo unique paths matched to the sample arm paths. This implementationtypically requires depth multiplexing at the detector and as such adetection method with sufficient depth range to accommodate the twosample arm images must be used.

Referring now to FIG. 5, a block diagram of a dual, full-field FDOCTsystem for combined fingernail bed and fingerprint imaging according tosome embodiments of the present inventive concept will be discussed. Asdiscussed above, like reference numerals refer to like elementsthroughout. Accordingly, the LCS 100, the optical coupler 110, thereference arm 120, the sample arm 200, the computer system 140, thedetector 151 and the sample 160 are similar to those discussed abovewith respect to FIG. 2. However, embodiments illustrated in FIG. 5include a second full-field sample arm 500, which uses a switchedreference arm 122 discussed above with respect to FIG. 9.

Referring now to FIG. 6, a block diagram of a raster scanning FDOCTsystem for iris imaging according to some embodiments of the presentinventive concept will be discussed. As discussed above, like elementsrefer to like elements throughout. Accordingly the LCS 100, the opticalcoupler 110, the reference arm 120, the sample arm 130, the computersystem 140, and the detector 150 are similar to those discussed abovewith respect to FIG. 1. However, as illustrated in FIG. 6, the system ofFIG. 6 is used to image an iris 600. Rectangular volume scans orcircular scans acquired through the iris may be used to capture depthslices through the iris according to some embodiments of the presentinventive concept as will be discussed further below.

Referring now to FIG. 7, a block diagram of a full-field FDOCT systemfor iris imaging according to some embodiments of the present inventiveconcept will be discussed. As discussed above, like reference numeralsrefer to like elements throughout. Accordingly, the LCS 100, the opticalcoupler 110, the reference arm 120, the sample arm 200, the computersystem 140 and the detector 150 are similar to those discussed abovewith respect to FIG. 2. However, as illustrated in FIG. 7, the system ofFIG. 7 is used to image an iris 600.

Referring to FIG. 8, a block diagram of a raster scanning FDOCT systemwith combined digital capture system according to some embodiments ofthe present inventive concept will be discussed. As discussed above,like elements refer to like elements throughout. Accordingly the LCS100, the optical coupler 110, the reference arm 120, the sample arm 130,the computer system 140, the detector 150 and the digital capture system300 are similar to those discussed above with respect to FIG. 3.However, as illustrated in FIG. 8, the system of FIG. 8 is used toacquire both FDOCT and digital capture 300 images of the iris 600.

Referring now to FIG. 11, images and graphs illustrating slice planesthrough a 3D volume in the fingernail bed according to some embodimentsof the inventive concept will be discussed. Some embodiments of thepresent inventive concept involve the generation of a unique code from a2-dimensional slice 1107 through a 3-dimensional volume 1100 acquired atthe fingernail bed. The orientation axes 1101 and correspondingfingernail orientation 1102 are illustrated in FIG. 11. The traditionalB-Scan consists of spatially unique locations along the azimuthdimension 1101 with a volume consisting of multiple B-Scans at spatiallyunique planes along the elevation dimension. A single depth line 1103(A-Scan) provides an intensity profile 1104 at a single, spatiallyunique scan position 1105. Transverse B-Scan slices 1106 provide imagesas a function of azimuth and depth 1107 through the nailbed 1108.Sagittal slices through the structure 1109 provide similar B-Scan images1110 through the nailbed 1111. Once an FDOCT volume has been acquired,coronal slices 1112 and corresponding C-Scan images 1113 can bereconstructed for the nailbed 1114. In some embodiments of the presentinventive concept, a raster scan orthogonal to the banded structure inthe fingernail bed may be used. By acquiring multiple scans orthogonalto the bands in the bed at different positions across the nail, a volumeof data 1100 is generated. Generating a projection along the depth axisreturns a volume intensity projection. Some embodiments of the presentinventive concept improve upon conventional methods by the addition ofthe depth dimension, which permits multiple methods of analysis.

Referring now to FIGS. 12A, B and C, graphs and images illustratinggeneration and evolution of the Gabor filter according to someembodiments of the present inventive concept will be discussed. Someembodiments of the present inventive concept use the Gabor wavelet asthe filtering function. Daugman proposes the use of the Gabor wavelet toanalyze the localized spatial frequency content of the warped irisimage. Similar processing may be applied to the banded structure of thefingernail bed. The 1-D Gabor filter is composed of a complexexponential windowed by a Gaussian 1200:

$\begin{matrix}{{f\left( {A,u,x,\sigma_{x}} \right)} = {{\exp \left( {{- 2}\pi \; {iux}} \right)}*{\exp\left( \frac{- \left( {x - x_{0}} \right)^{2}}{\sigma_{x}^{2}} \right)}}} & \left\lbrack {{Eqn}.\mspace{14mu} 1} \right\rbrack\end{matrix}$

where f(A,u,x,σ_(x)) is the filtering function, A is the amplitude ofthe function, i is the imaginary variable, u is the spatial frequency, xis the space variable, x₀ is some offset in space that defines thecenter of the Gaussian envelope, and σ_(x) is the standard deviationthat defines the width of the Gaussian envelope. The real and imaginaryparts 1201 are sinusoids windowed by a Gaussian function.

The 2-dimensional extension of Eqn. 1 becomes Eqn. 2:

${f\left( {A,u,v,x,y,\sigma_{x},\sigma_{y}} \right)} = {A\; {\exp\left( {{- 2}\pi \; {i\left( {{ux} + {vy}} \right)}^{*}{\exp\left( \frac{- \left( {x - x_{0}} \right)^{2}}{\sigma_{x}^{2}} \right)}*{\exp\left( \frac{- \left( {y - y_{0}} \right)^{2}}{\sigma_{y}^{2}} \right)}} \right.}}$

where f(A,u,v,x,y,σ_(x)σ_(y)) is the filtering function, A is theamplitude of the function, i is the imaginary variable, x and y are thespace variables and represent the spatial axes along which the image iscollected, x₀ and y₀ are offsets in space that define the center of theGaussian envelope and are typically set to the center of the Gaborwavelet window, u and v are the spatial frequencies of the complexexponential term and are typically set to some range of spatialfrequencies that overlap with spatial frequencies that are likely tooccur within the imaged tissue, and σ_(x) and σ_(y) are the standarddeviations that define the width of the Gaussian envelope along thespatial axes. The real 1202 and imaginary parts are 2D sinusoidswindowed by a 2D Gaussian function.

In some embodiments of the present inventive concept, x is typicallymapped to the azimuth scan dimension and y is typically mapped to thedepth dimension. If the range of x is −2 to 2 millimeters and the rangeof y is 0 to 2 mm, then x₀ is set to 0 mm and y₀ is set to 1 mm tocenter the Gabor wavelet in the image window. If spatially varying bandswithin the tissue have spatial frequencies ranging from 0.01 to 0.05mm⁻¹ along the azimuthal dimension and 0.2 to 0.3 mm⁻¹ in the depthdimension, then an appropriate range for u would be 0.005 to 0.1 mm⁻¹and an appropriate range for v would be 0.1 to 0.4 mm⁻¹. If the regionthat contains the bands is 1 mm in azimuth and 0.5 mm in depth, thenσ_(x) and σ_(y) may be chosen such that that full width at half maximumof the Gaussian envelope covers this range.

Referring now to FIG. 13, diagrams illustrating decision code generationaccording to some embodiments of the present inventive concept will bediscussed. To generate a unique code based on the spatial frequencycontent of the fingernail bands 1300, the image is warped to flatten thenail 1301 and a region 1302 is extracted from that structure.

Scaled versions of a 2-D Gabor wavelet 1303 are applied to the extractedstructure 1302. The Gabor wavelet may be cross-correlated with theflattened structure in the spatial dimension or the Fourier transformsof the Gabor wavelet and the flattened structure may be multiplied inthe spatial frequency dimension.

The width of the light and dark bands within the extracted structure maybe from about 0.13 mm to about 0.25 mm Thus, the spatial frequencycontent of the Gabor filter should be varied in fine increments overmultiple scales of the Nyquist limit, which in this case is may be about0.065 mm⁻¹. The standard deviation of the Gaussian, σ, should be variedin multiple scales around the known width of the bands.

For each set of filter settings, a real and imaginary filtered image aregenerated by multiplying the 2D Gabor filter with the extracted bands.As discussed in Daugman, the phase quadrature response of the data tothe filter set is extracted as:

h=h _(re) +i·h _(im)  [Eqn. 3]

where h_(re) is the sign of the integrated response to the real part ofthe Gabor filter and h_(im) is the sign of the integrated response tothe imaginary part. The phase of h yields coordinates in the complexplane that are mapped to bit pairs where (1,1), (0,1), (0,0), (1,0) aremapped to quadrants I-IV, respectively 1304.

The process is repeated across multiple scales of the spatial frequencyfilter 1306 to generate a binary code representative of imageinformation content 1307.

Referring now to FIG. 14, a diagram illustrating decision codeprocessing and analysis according to some embodiments of the presentinventive concept will be discussed. As illustrated therein, binary mapsare reshaped from 2-dimensional arrays 1400 to 1-dimensional arrays 1401and are compared using the Hamming distance as a measure of uniqueness1402. The Hamming distance is:

$\begin{matrix}{d = \frac{A \oplus B}{{length}(A)}} & \left\lbrack {{Eqn}.\mspace{14mu} 4} \right\rbrack\end{matrix}$

where A and B are the binary maps and ⊕ is the exclusive or (XOR)operation. For each bit that is dissimilar, the XOR operation returnstrue, and as such the Hamming distance is greater for unique maps.

For two maps that are exactly the same, the Hamming distance should beabout 0, a map processed with its inverse returns a Hamming distance of1, and two randomly generated binary maps should return a Hammingdistance of 0.5.

Similar comparison methods, such as the cross-correlation, can beapplied to determine the relationship between codes 1403. Thecorrelation peak may then be used to determine the similarity betweencodes.

A decision threshold can be generated based on a database of truepositives and their resultant Hamming distance. For a given codegeneration method, the Hamming distance distribution 1404 will have aunique shape, and a decision line may be calculated to yield a desiredsensitivity and specificity.

Referring now to FIG. 15, a diagram illustrating unique code generationfrom a slice plane through a 3D volume of fingernail bed data accordingto some embodiments of the present inventive concept will be discussed.In particular, methods of slicing data to best analyze the spatialfrequency content of the tissue topology under the fingernail bed willbe discussed. Scans acquired orthogonal to the banded structure of thenail bed are flattened 1500 by finding the contour of the inner surfaceof the fingernail and warping the image based on the contour. A filter1501 can then be applied to the data to extract or emphasize informationcontent not readily available in the original image data. The bandedstructure can then be extracted from the area under the fingernail andprocessed 1502 to return a code 1503 unique to not only each individualbut to each finger as well.

FIG. 15 illustrates the processing involved in the image analysis. Theprocessing detailed above is applied to the extracted region, and abinary pair is generated for each set of filter values, yielding a mapof bit values as illustrated in the code 1503 that is directly relatedto the unique spatial frequency content contained in each finger'spattern.

Referring now to FIG. 16, a series of images illustrating OCT slicesseparated in time through finger 9 and the subsets used for unique,matching code generation according to some embodiments of the presentinventive concept will be discussed. A depth slice may be acquired fromone finger 1600 at two different time points 1601, 1603 and subsets1602, 1604 from the two slices can be analyzed to determine the Hammingdistance for identical finger scans as a function of time. Afteracquisition of many such scans, the Hamming distance range for uniquefingers may be statistically determined for large data sets.

Referring now to FIG. 17, a series of images illustrating OCT slicesfrom fingers 9 and 4 and the subsets used for unique, non-matching codegeneration according to some embodiments of the present inventiveconcept will be discussed. Depth slices 1601, 1701 may be acquired fromtwo unique fingers 1600, 1700 on different hands and subsets 1602, 1702from the two slices can be analyzed to determine the Hamming distancefor different fingers. After acquisition of many such scans, the Hammingdistance range for uniquely different fingers on different hands may bestatistically determined for large data sets.

Referring now to FIG. 18, a series, of images illustrating OCT slicesfrom fingers 3 and 4 and the subsets used for unique, non-matching codegeneration according to some embodiments of the present inventiveconcept will be discussed. Depth slices 1701, 1801 may be acquired fromtwo unique fingers 1700, 1800 on the same hand and subsets 1702, 1802from the two slices can be analyzed to determine the Hamming distancefor different fingers on the same hand. After acquisition of many suchscans, the Hamming distance range for uniquely different fingers on thesame hand may be statistically determined for large data sets.

A database may be generated from many codes captured from the samefinger as a function of time to determine the variability in the Hammingdistance as a function of time and unique spatial position scanned todetermine the range of Hamming distances that may be assigned to apositive identification.

A database may be generated from many codes captured from differentfingers as a function of time to determine the variability in theHamming distance as a function of time and unique spatial content todetermine the range of Hamming distances likely in a negativeidentification.

The true positive and true negative ranges may be modeled by a normaldistribution to approximate the false positive and false negativeranges.

A receiver operating characteristic (ROC) curve may be generated basedon the true positives and estimated false positives to highlight thesensitivity and specificity of the code as implemented.

Referring now to FIG. 19, a flowchart illustrating processing steps inhigh-throughput biometric identification in accordance with someembodiments of the present inventive concept will be discussed. Operatesbegin at block 1900 by logging into the system. After logging into thesecurity system (block 1900), the display will be cleared (block 1901)and the user will be prompted to start the exam (block 1902). Thesubject will place one or more fingers in the path of the scan beam andthe necessary OCT data will be acquired (block 1903). This data willthen be processed by extracting a sub-region of the OCT image (block1904) and generating the code (block 1905) associated with each uniquefinger. Smart search algorithms will examine a database of stored binarymaps to determine if a match exists within the database (block 1906). Ifa match exists (block 1906) and the match is recognized above athreshold confidence level (block 1907), the software will displayrelevant subject information to the user (block 1909). If no matchexists within the database (block 1907), the option will be presented tothe user to add the subject to the database (block 1908). It will bedetermined if another finger needs to be examined (block 1911). Ifanother finger needs to be examined (block 1911), operations return toblock 1903 and repeat. If, on the other hand, no other fingers need tobe examined, operations of the exam may terminate (block 1910). In someembodiments, the software may be expanded to incorporate advancedsecurity measures, which would include but not be limited to interfacingto national security software for more detailed subject information suchas any outstanding warrants or alerting law enforcement if a wantedsubject has been identified. The results, consisting of either theprocessed OCT data and the resultant code or only the resultant code maybe stored on a local or networked computer.

Referring to FIG. 20, a series of slice planes through a 3D volume inthe iris and representative OCT images from said planes according tosome embodiments of the inventive concept will be discussed. Someembodiments involve the generation of a unique code from a 2-dimensionalslice 2006 through a 3-dimensional volume 2000 acquired at the iris. Theorientation axes 2001 and corresponding iris orientation 2002 aredetailed in FIG. 20. A single depth line 2003 (A-Scan) provides anintensity profile 2004 at a single, spatially unique scan position 2005.Transverse B-Scan slices 2006 provide images as a function of azimuthand depth 2007 through the iris 2008. Circular slices through thestructure 2009 provide B-Scan images 2010 through the iris 2011. Once anFDOCT volume has been acquired, coronal slices 2012 and correspondingC-Scan images 2013 can be reconstructed for the iris 2014. In someembodiments of the present inventive concept, a raster scan orthogonalto the banded structure in the fingernail bed may be used. By acquiringmultiple scans orthogonal to the bands in the bed at different positionsacross the nail, a volume of data 2000 is generated. Generating aprojection along the depth axis returns a volume intensity projection.Some embodiments of the present inventive concept improve uponconventional methods by the addition of the depth dimension, whichpermits multiple methods of analysis.

Referring now to FIG. 21, diagrams of unique code generation from 1slice through a 3D volume of iris data according to some embodiments ofthe inventive concept will be discussed. In particular, methods ofslicing data to best analyze the spatial frequency content of the tissuetopology of the iris will be discussed. Circular scans acquired at theiris are flattened 2100 by finding the contour of the inner surface ofthe iris and warping the image based on the contour. A filter 2101 canthen be applied to the data to extract or emphasize information contentnot readily available in the original image data. The fine structure canthen be extracted from the iris and processed 2102 to return a code 2103unique to not only each individual but to each eye as well.

The code generated by some embodiments of the present inventive conceptmay be used to aid in subject identification. This code could beincluded with any form of personal identification, such as a passport orgovernment-issued ID, as an additional security measure. As such, thecode could be, for example, read by a barcode scanner interfaced to theOCT database to aid in rapid identification and screening inhigh-traffic security checkpoints.

While some embodiments of the present inventive concept are discussedabove, other embodiments may also be envisioned without departing fromthe scope of the present inventive concept. For example, furtherembodiments of the present inventive concept are illustrated in FIGS.1106, 1109, and 1112. In particular, a slice orthogonal to the depthaxis 1112 would return the band pattern as seen from the nail. A slice1106, would yield a side-long view of the banded structure. Samplingmore densely in this dimension could allow for better resolution of thespatial frequency content contained in the bands. With the volume ofdata, it may be possible to analyze the data using embodiments discussedherein and then use an additional implementation, such as the analysisusing slices 1106, to improve the confidence level of the result.

Furthermore, traditional fingerprint data may be collected using systemsaccording to some embodiments of the present inventive concept. An SDOCTvolume may be acquired over the finger tip; the volume projection ofsuch a volume would yield an image containing traditional fingerprintdata. In further embodiments, this data could be acquired by scanningthe top of the nail with the SDOCT system while at the same timecapturing an image of the finger tip using a standard still or videocamera. With both data types captured concurrently, the newSDOCT-generated biometric could be correlated with and stored alongsidetraditional fingerprint data contained in law enforcement databases,facilitating easier integration into current security systems.

As discussed briefly above, methods, systems and computer programproducts for biometric identification by human fingernail bed imagingusing SDOCT are non-invasive methods, systems and computer programproducts for acquiring a novel biometric identifier that is relativelyinsensitive to obfuscation by cosmetic means. This technology issynergistic with traditional fingerprint identification systems andcould interface with said systems easier than other biometrictechnologies.

As discussed above, data acquired using systems and methods according tosome embodiments of the present inventive concept may be processed usinga computer system 140 (data processing system). Exemplary embodiments ofa data processing system 2230 configured in accordance with embodimentsof the present inventive concept will be discussed with respect to FIG.22. The data processing system 2230 may include a user interface 2244,including, for example, input device(s) such as a keyboard or keypad, adisplay, a speaker and/or microphone, and a memory 2236 that communicatewith a processor 2238. The data processing system 2230 may furtherinclude I/O data port(s) 2246 that also communicates with the processor2238. The I/O data ports 2246 can be used to transfer informationbetween the data processing system 2230 and another computer system or anetwork using, for example, an Internet Protocol (IP) connection. Thesecomponents may be conventional components such as those used in manyconventional data processing systems, which may be configured to operateas described herein.

Referring now to FIG. 23, a more detailed block diagram of a dataprocessing system of FIG. 22 is provided that illustrates systems,methods, and computer program products in accordance with someembodiments of the present inventive concept, which will now bediscussed. As illustrated in FIG. 23, the processor 2238 communicateswith the memory 2236 via an address/data bus 2348, the I/O data ports2246 via address/data bus 2349 and the electronic display 2339 viaaddress/data bus 2350. The processor 2238 can be any commerciallyavailable or custom enterprise, application, personal, pervasive and/orembedded microprocessor, microcontroller, digital signal processor orthe like. The memory 2236 may include any memory device containing thesoftware and data used to implement the functionality of the dataprocessing system 2230. The memory 2236 can include, but is not limitedto, the following types of devices: ROM, PROM, EPROM, EEPROM, flashmemory, SRAM, and DRAM.

As further illustrated in FIG. 23, the memory 2236 may include severalcategories of software and data used in the system: an operating system2352; application programs 2354; input/output (I/O) device drivers 2358;and data 2356. As will be appreciated by those of skill in the art, theoperating system 2352 may be any operating system suitable for use witha data processing system, such as OS/2, AIX or zOS from InternationalBusiness Machines Corporation, Armonk, N.Y., Windows95, Windows98,Windows2000 or WindowsXP, or Windows CE or Windows 7 from MicrosoftCorporation, Redmond, Wash., Palm OS, Symbian OS, Cisco IOS, VxWorks,Unix or Linux. The I/O device drivers 2358 typically include softwareroutines assessed through the operating system 2352 by the applicationprograms 2354 to communicate with devices such as the I/O data port(s)2246 and certain memory 2236 components. The application programs 2354are illustrative of the programs that implement the various features ofthe some embodiments of the present inventive concept and may include atleast one application that supports operations according to embodimentsof the present inventive concept. Finally, as illustrated, the data 2356may include acquired scans 2359, subsets 2360, filtered images 2361 andcodes 2362, which may represent the static and dynamic data used by theapplication programs 2354, the operating system 2352, the I/O devicedrivers 2358, and other software programs that may reside in the memory2236.

As further illustrated in FIG. 23, according to some embodiments of thepresent inventive concept, the application programs 2354 include OCTimaging modules 2365. While the present inventive concept is illustratedwith reference to OCT imaging modules 2365 as being application programsin FIG. 23, as will be appreciated by those of skill in the art, otherconfigurations fall within the scope of the present inventive concept.For example, rather than being application programs 2354, these circuitsand modules may also be incorporated into the operating system 2352 orother such logical division of the data processing system. Furthermore,while the OCT imaging modules 2365 are illustrated in a single system,as will be appreciated by those of skill in the art, such functionalitymay be distributed across one or more systems. Thus, the presentinventive concept should not be construed as limited to theconfiguration illustrated in FIG. 23, but may be provided by otherarrangements and/or divisions of functions between data processingsystems. For example, although FIG. 23 is illustrated as having variouscircuits, one or more of these circuits may be combined withoutdeparting from the scope of the present inventive concept.

It will be understood that the OCT imaging modules 2365 may be used toimplement various portions of the present inventive concept capable ofbeing performed by a data processing system. For example, the OCTimaging modules may be used to process and assess the images produced bythe OCT system according to some embodiments of the present inventiveconcept.

Example embodiments are described above with reference to block diagramsand/or flowchart illustrations of methods, devices, systems and/orcomputer program products. It is understood that a block of the blockdiagrams and/or flowchart illustrations, and combinations of blocks inthe block diagrams and/or flowchart illustrations, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, and/or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer and/or other programmable data processingapparatus, create means (functionality) and/or structure forimplementing the functions/acts specified in the block diagrams and/orflowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instructions whichimplement the functions/acts specified in the block diagrams and/orflowchart block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe block diagrams and/or flowchart block or blocks.

Accordingly, example embodiments may be implemented in hardware and/orin software (including firmware, resident software, micro-code, etc.).Furthermore, example embodiments may take the form of a computer programproduct on a computer-usable or computer-readable storage medium havingcomputer-usable or computer-readable program code embodied in the mediumfor use by or in connection with an instruction execution system. In thecontext of this document, a computer-usable or computer-readable mediummay be any medium that can contain, store, communicate, propagate, ortransport the program for use by or in connection with the instructionexecution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: an electricalconnection having one or more wires, a portable computer diskette, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,and a portable compact disc read-only memory (CD-ROM). Note that thecomputer-usable or computer-readable medium could even be paper oranother suitable medium upon which the program is printed, as theprogram can be electronically captured, via, for instance, opticalscanning of the paper or other medium, then compiled, interpreted, orotherwise processed in a suitable manner, if necessary, and then storedin a computer memory.

Computer program code for carrying out operations of data processingsystems discussed herein may be written in a high-level programminglanguage, such as Java, AJAX (Asynchronous JavaScript), C, and/or C++,for development convenience. In addition, computer program code forcarrying out operations of example embodiments may also be written inother programming languages, such as, but not limited to, interpretedlanguages. Some modules or routines may be written in assembly languageor even micro-code to enhance performance and/or memory usage. However,embodiments are not limited to a particular programming language. Itwill be further appreciated that the functionality of any or all of theprogram modules may also be implemented using discrete hardwarecomponents, one or more application specific integrated circuits(ASICs), or a field programmable gate array (FPGA), or a programmeddigital signal processor, a programmed logic controller (PLC), ormicrocontroller.

It should also be noted that in some alternate implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand/or block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and/or blockdiagrams may be at least partially integrated.

In some embodiments of the present inventive concept, the uniquereference code generated for a sample may be used in making a diagnosticdecision. In particular, the reference codes may be indicative of aclass of objects. As used herein, a “class of objects” refers to anyobject for which a multiple dimensional image can be acquired and aunique code can be generated. The class of objects may be defined by aregular structure that is nominally time invariant. As discussed above,“time invariant” refers to a property that does not change over a timeperiod of interest. For example, the class of objects may include aphysical state, a physical object (an integrated circuit, a printedcircuit board, etc.), human tissue and the like. Each class of objectshas a reference code, which is derived from aggregate data.

The subject of the image is not time-invariant. Thus, a unique referencecode may be generated for the subject being imaged and this referencecode can be compared to reference codes for each class. The result ofthis comparison can be used to provide a diagnosis. For example, if thesubject being imaged is a circuit board, embodiments discussed hereinmay be used for pattern recognition. Thus, if a trace on the circuitboard is broken or out of place, this diagnosis can be made. For a humansubject, it may be determined that a subject falls into a certain classor the subject's class has changed over time. Falling into the class ora change of class may be indicative of a disease state or of diseaseprogression.

Thus, a state is a condition of being that is identifiable and hasattributes that can be captured in a multidimensional image. The statemust be sufficiently consistent over time to have meaning. Accordingly,embodiments of the inventive concept may be used to diagnose diseases ina human subject, identify manufacturing failures in a manufacturedproduct, identify people using facial recognition and the like as willbe discussed further below with respect to FIGS. 24 through 37.

Referring first to FIG. 24, a block diagram illustrating processingN-Dimensional data in accordance with some embodiments of the presentinventive concept will be discussed. As illustrated in FIG. 24,N-Dimensional data 2400, 2401, 2402 can be processed as-is or collapsedto a lower dimensional order 2403 with a kernel operation 2404 thatextracts unique identifiers from the data set 2403 and generates aunique code 2405 associated with the data set 2403. This code can thenbe compared 2406 with other unique identifier codes and a decision 2407can be made based on the relationship between the code generated 2405and the reference code or codes.

Referring now to FIG. 25, data to be processed 2403 (FIG. 24) can beN-Dimensional. For example, the data to be processed 2403 may be 1dimensional 2500, i.e., originating from one unique location in space; 1dimensional+Time 2501, i.e., originating from one unique location inspace as measured over some finite period of time; 2 dimensional 2502,i.e., originating from multiple spatial locations along a plane orsurface; 2 dimensional+Time 2503, i.e., originating from a plane orsurface as measured over some finite period of time; 3 dimensional 2504,i.e., originating from multiple spatial locations contained within athree dimensional volume; or 3 dimensional+Time 2505, i.e., originatingfrom multiple spatial locations contained within a three dimensionalvolume as measured over some finite period of time. It will beunderstood that this dimensional data is provide for example only andthat embodiments of the present inventive concept are not limitedthereto.

Referring now to FIG. 26, the kernel applied 2404 (FIG. 24) may matchthe dimensionality of the data 2403 (FIG. 24) along 1 dimensional 2600;1 dimensional+Time 2601; 2 dimensional 2602; 2 dimensional+Time 2603; 3dimensional 2604; or 3 dimensional+Time 2605 spaces.

Referring now to FIG. 27, reference codes 2406 (FIG. 24) may begenerated by acquiring data of the desired observable 2700 in the formof an image 2701 or other data set, and applying a kernel 2702 toextract feature classes of interest 2703, for example, Class 1, Class 2,Class N and the like. In some embodiments, kernels 2702 are optimizedacross many data sets to increase the likelihood that backgroundvariability between data sets containing the feature of interest allowsfor a clear decision threshold when the feature is or is not present.Once the kernels 2702 have been created, reference codes 2704 for eachfeature class 2703 may be created along with a decision threshold 2705plot to indicate how close an input code 2405 (FIG. 24) must be to theclass reference code to be included within the class (positive result)or excluded from the class (negative result). These kernels can beapplied 2404 (FIG. 24) to the input data 2403 (FIG. 24) and comparedagainst the reference codes generated 2704 to determine whether or notthe feature class exists within the input data 2403 (FIG. 24).

As used herein, the “observable” 2700 may be a measured or derivedparameter that may include, for example, spectra, structure, phase,flow, polarization, scattering coefficient or cross-section, anisotropy,or other biologically relevant optical parameters.

Kernels used 2702 may be class-specific and optimized to extractinformation content from the input data 2701 that is more likely toexist within a specific class 2703 and will select a specific class withhigh confidence. Other kernels 2702 may be more generic and targeted atdetermining class membership in one or more classes with lowerconfidence. For example, kernel 1 may be configured to target a specificfeature within the input data and determines whether or not the inputdata contains that feature, while kernel 2 may be configured to looselytarget multiple features within the input data to determine whether ornot the input data may belong to a collection of classes.

A more detailed example will now be discussed. For example, kernel 1 maybe configured to select for spatial frequencies that are indicative oftortuous vessels in diabetic retinopathy. Images with a low correlationwith the “Human adult retina diabetic retinopathy tortuousityclassifier” have a level or tortuosity (or lack of tortuosity) thatindicates they may not have diabetic retinopathy. Kernel 2 may beconfigured to select for the general spatial frequency content andtextural information associated with adult human retinal images. Imageswith a high correlation to the “Human adult retina general classifier”code are most likely human adult retina images.

As an extension, a family of filter kernels and classifier codes may begenerated for disease states and run against the code generated from aninput data set to determine if the data set may show some of thefeatures indicative of one or more of the disease states. The sametechnique may be applied with a more general kernel family to determinein a broader sense an image classification such as, “adult human retina”or “adult human cornea.”

Referring now to FIG. 28, the input code 2405 (FIG. 24) may be comparedto a library of reference codes 2704 (FIG. 27) using, for example,cross-correlation or other integral or binary operation. The resultantfunction 2800, which may be a cross-correlation function, can beobtained for all codes in the reference library 2704 (FIG. 27) or allcodes within a desired feature class. Each feature class may be linkedwith a decision curve 2801 as generated during the reference librarycreation process 2705 (Decision Thresholds—FIG. 27). If the peak of theresultant function 2800 lies within the positive decision region of only1 code, then the input data 2403 (FIG. 24) may be deemed to containfeatures known to reside in the matched class 2802 (identification).

Referring now to FIG. 29, if the input code 2405 (FIG. 24) producescorrelation (or other functional output) peaks 2900 that reside withinthe same region of the decision threshold of multiple codes 2901, thenadditional processing 2902 may be applied to determine which class isthe closest fit for the code, flag the code as ambiguous or belonging tomultiple classes, or return to the code generation phase 2404 (FIG. 24)with improved or modified kernel settings that may help in selection ofa single class 2802 (FIG. 28). The confidence or relative relation toeach of the reference codes may be reported to the user forsemi-automated decision selection 2903.

Referring now to FIG. 30, N-Dimensional data 3000 may require additionalprocessing 3001 before applying a generic or class-specific kernel 3002to the N or N-m dimensional data. After a code is generated 3003,additional processing 3004 may be required before comparison 3006 withone or more reference codes 3005 before a decision or identification ismade 3007.

Referring now to FIG. 31, images and graphs illustrating slice planesthrough a 3D volume in the fingerprint according to some embodiments ofthe inventive concept will be discussed. Some embodiments of the presentinventive concept involve the generation of a unique code from a2-dimensional slice 3107 through a 3-dimensional volume 3100 acquiredfrom the fingerprint. The orientation axes 3101 and correspondingfingerprint orientation 3102 are illustrated in FIG. 31. The traditionalB-Scan consists of spatially unique locations along the azimuthdimension 3101 with a volume consisting of multiple B-Scans at spatiallyunique planes along the elevation dimension 3101. A single depth line3103 (A-Scan) provides an intensity profile 3104 at a single, spatiallyunique scan position 3105. Transverse B-Scan slices 3106 provide imagesas a function of azimuth and depth 3107 through the fingerprint 3108.Sagittal slices through the structure 3109 provide similar B-Scan images3110 through the fingerprint 3111. Once an FDOCT volume has beenacquired, coronal slices 3112 and corresponding C-Scan images 3113 canbe reconstructed for the fingerprint 3114. In some embodiments of thepresent inventive concept, a raster scan orthogonal to the bandedstructure in the fingerprint may be used. By acquiring multiple scansorthogonal to the bands in the fingerprint at different positions acrossthe fingerprint, a volume of data 3100 is generated. Generating aprojection along the depth axis returns a volume intensity projection.Some embodiments of the present inventive concept improve uponconventional methods by the addition of the depth dimension, whichpermits multiple methods of analysis.

FIG. 32 illustrates the processing involved in the image analysis. Theprocessing detailed above is applied to the extracted region, and abinary pair is generated for each set of filter values, yielding a mapof bit values as illustrated in the code 3203 that is directly relatedto the unique spatial frequency content contained in each fingerprint'spattern.

In particular, diagrams of unique code generation from 1 slice through a3D volume of fingerprint data according to some embodiments of theinventive concept will be discussed. In particular, methods of slicingdata to best analyze the spatial frequency content of the tissuetopology of the fingerprint will be discussed. Circular scans acquiredof the fingerprint are flattened 3200 by finding the contour of theinner surface of fingerprint and warping the image based on the contour.A filter 3201 can then be applied to the data to extract or emphasizeinformation content not readily available in the original image data.The fine structure can then be extracted from the fingerprint andprocessed 3202 to return a code 3203 unique to not only each individualbut to each fingerprint as well.

Referring now to FIG. 33, images and graphs illustrating slice planesthrough a 3D volume in the retina according to some embodiments of thepresent inventive concept will be discussed. B-scans shown in FIG. 33are examples meant to illustrate the approximate appearance of the dataalong each plane; the B-scan under the sagittal slice illustration isactually a transverse section. The image under the C-scan is actually aprojection along the depth axis rather than an individual slice alongthe coronal plane.

As illustrated in FIG. 33, some embodiments of the present inventiveconcept involve the generation of a unique code from a 2-dimensionalslice 3307 through a 3-dimensional volume 3300 acquired at the retina.The orientation axes 3301 and corresponding retina orientation 3302 areillustrated in FIG. 33. The traditional B-Scan consists of spatiallyunique locations along the azimuth dimension 3301 with a volumeconsisting of multiple B-Scans at spatially unique planes along theelevation dimension. A single depth line 3303 (A-Scan) provides anintensity profile 3304 at a single, spatially unique scan position 3305.Transverse B-Scan slices 3306 provide images as a function of azimuthand depth 3307 through the retina 3308. Sagittal slices through thestructure 3309 provide similar B-Scan images 3310 through the retina3311. Once an FDOCT volume has been acquired, coronal slices 3312 andcorresponding C-Scan images 3313 can be reconstructed for the retina3314. In some embodiments of the present inventive concept, a rasterscan orthogonal to the banded structure in the retina may be used. Byacquiring multiple scans orthogonal to the bands in the retina atdifferent positions across the retina, a volume of data 3300 isgenerated. Generating a projection along the depth axis returns a volumeintensity projection. Some embodiments of the present inventive conceptimprove upon conventional methods by the addition of the depthdimension, which permits multiple methods of analysis.

FIG. 34 illustrates the processing involved in the image analysis. Theprocessing detailed above is applied to the extracted region, and abinary pair is generated for each set of filter values, yielding a mapof bit values as illustrated in the code 3403 that is directly relatedto the unique spatial frequency content contained in each retina'spattern. The choroidal region in FIG. 34 is selected and processed froma 2D frame of image data and the code generated from that image data.

In FIG. 35, the choroidal region is selected along a slice planeorthogonal to the depth axis, for example, horizontal line from everyimage in a volume was used to generate image 3500, from which the codeis generated.

Referring now to FIG. 36, images and graphs illustrating slice planesthrough a 3D volume in the retina according to some embodiments of thepresent inventive concept will be discussed. B-scans shown in FIG. 36are examples meant to illustrate the approximate appearance of the dataalong each plane; the B-scan under the sagittal slice illustration isactually a transverse section. The image under the C-scan is actually aprojection along the depth axis rather than an individual slice alongthe coronal plane.

As illustrated in FIG. 36, some embodiments of the present inventiveconcept involve the generation of a unique code from a 2-dimensionalslice 3607 through a 3-dimensional volume 3600 acquired at the retina(vessels). The orientation axes 3601 and corresponding retinaorientation 3602 are illustrated in FIG. 36. The traditional B-Scanconsists of spatially unique locations along the azimuth dimension 3601with a volume consisting of multiple B-Scans at spatially unique planesalong the elevation dimension. A single depth line 3603 (A-Scan)provides an intensity profile 3604 at a single, spatially unique scanposition 3605. Transverse B-Scan slices 3606 provide images as afunction of azimuth and depth 3607 through the retina 3608. Sagittalslices through the structure 3609 provide similar B-Scan images 3610through the retina 3611. Once an FDOCT volume has been acquired, coronalslices 3612 and corresponding C-Scan images 3613 can be reconstructedfor the retina 3614. In some embodiments of the present inventiveconcept, a raster scan orthogonal to the banded structure in the retinamay be used. By acquiring multiple scans orthogonal to the bands in theretina at different positions across the retina, a volume of data 3600is generated. Generating a projection along the depth axis returns avolume intensity projection. Some embodiments of the present inventiveconcept improve upon conventional methods by the addition of the depthdimension, which permits multiple methods of analysis.

Referring now to FIG. 37, processing involved in the image analysis ofthe retina vessels will be discussed. The processing detailed above isapplied to the extracted region, and a binary pair is generated for eachset of filter values, yielding a map of bit values as illustrated in thecode 3703 that is directly related to the unique spatial frequencycontent contained in each retina's pattern.

In the drawings and specification, there have been disclosed exemplaryembodiments of the inventive concept. However, many variations andmodifications can be made to these embodiments without substantiallydeparting from the principles of the present inventive concept.Accordingly, although specific terms are used, they are used in ageneric and descriptive sense only and not for purposes of limitation,the scope of the inventive concept being defined by the followingclaims.

That which is claimed is:
 1. A method of providing a diagnosis using adigital code associated with an image, the method comprising: collectinga multidimensional image of a subject, the multidimensional image havingat least two dimensions; extracting a two dimensional subset of themultidimensional image; reducing the multidimensional image to a firstcode that is unique to the multidimensional image of the subject basedon the extracted two dimensional image; comparing the first unique codeassociated with the subject to a library of reference codes, each of thereference codes in the library of reference codes being indicative of aclass of objects; determining if the subject associated with the firstunique code falls into at least one of the classes of objects associatedwith the reference codes based on a result of the comparison; andformulating a diagnostic decision based on the whether the first uniquecode associated with the subject falls into at least one of the classesassociated with the reference code.
 2. The method of claim 1: whereindetermining if the subject associated with the first unique referencecode falls into at least one of the classes further includes determiningif the subject associated with the first unique reference code haschanged classes over time; and wherein formulating the diagnosticdecision comprises formulating the diagnostic decision based on thechange of class over time.
 3. The method of claim 1, wherein determiningif the subject associated with the first unique code falls into at leastone of the classes of objects comprises determining that the subjectassociated with the first unique code falls into at least two of theclasses, the method further comprising: applying additional processingto determine which of the at least two classes more accuratelyidentifies the subject associated with the first unique code.
 4. Themethod of claim 1, wherein the classes associated with the referencecode identify at least one of a physical state and a physical object. 5.The method of claim 4; wherein the subject comprises at least one of afingernail bed, a fingerprint, an iris, a cornea, a retina, retinalvessels, any human tissue and physical object.
 6. The method of claim 1,wherein reducing further comprises reducing the two dimensional subsetto the first unique code based on a defined set of structural orfunctional information contained with the image, the method furthercomprising: storing the first unique code; and comparing the firstunique code to a second unique code to establish a degree of equivalenceof the first and second codes.
 7. The method of claim 1, wherein themultidimensional image comprises at least one of a volumetric imagerepresentative of time invariant information in a sample, slowlytime-variant information in a sample, or time variant structural orfunctional information in a sample.
 8. The method of claim 6, wherereducing the two dimensional subset to the first unique code comprises:manipulating the multidimensional data to extract a region of interest;extracting the structural or the functional information using a filter;translating the filtered information into the first unique coderepresentative of the information content contained in themultidimensional data; manipulating the first unique code to be comparedto other codes; and comparing two or more codes to assess a degree ofequivalents between the codes.
 9. The method of claim 8, whereinextracting comprises extracting using a filter configured to extract thestructural or functional information.
 10. The method of claim 9, whereinthe filter comprises at least one of a Gabor filter, a complex filterthat consists of real and imaginary parts, a complex filter thatconsists of a spatial frequency component and a Gaussian windowcomponent and a complex filter that has at least three unique degrees offreedom including amplitude, at least one spatial frequency, and atleast one Gaussian window standard deviation.
 11. The method of claim 9,wherein the filter is configured to operate on a two dimensional subsetusing at least one of a convolution in the native domain or amultiplication of the Fourier Transforms of the filter and twodimensional subset and multiple filter scales in which one or morefilter degrees of freedom are changed before combination with the imagesubset.
 12. The method of claim 9, wherein the unique code is obtainedfrom complex data comprising at least one of a complex representation ofthe filter combined with the image subset in which the complex result istreated as a vector in the complex plane and a method in which the angleof the vector in the complex plane is determined and a coderepresentative of the quadrant in the complex plane containing thevector angle is generated.
 13. The method of claim 1, wherein the uniquecode is binary such that each pixel of information is represented by oneof two states.
 14. The method of claim 1, wherein the unique code has abase greater than
 2. 15. The method of claim 1, wherein the unique codeis represented as a one or two dimensional barcode configured to be readby a generic commercial barcode reading technology.
 16. The method ofclaim 6, wherein comparing comprises comparing two or more unique codesusing cross-correlation or other relational comparison.
 17. The methodof claim 16, wherein the relational comparison comprises an XORoperation.
 18. The method of claim 16, wherein the comparison result isapplied to find a Hamming distance.
 19. The method of claim 18, whereinthe Hamming distance is used to validate a strength of the comparisonagainst a database of other codes.
 20. The method of claim 8, furthercomprising: repeating extracting, translating, manipulating, comparingand assigning to construct a database of codes; and defining a uniqueidentifier threshold based on sensitivity analysis of the codes in thedatabase.
 21. The method of claim 20, further comprising determining ifa calculated code is unique or present in the database by comparing theunique identifier threshold to the Hamming distance between thecalculated code and the codes in the database.
 22. The method of claim9, wherein the filter comprises any image processing system in whichinformation content is emphasized or extracted from a depth-dependentimage.
 23. The method of claim 22, wherein the filter comprises at leastone of a speckle tracking algorithm and a texture-based image analysisalgorithm.
 24. The method of claim 1, wherein the method is performedusing an optical coherence tomography (OCT) imaging system.
 25. Acomputer program of providing a digital code associated with an image,the computer program product comprising computer program code embodiedin a computer readable medium, the computer program code comprisingprogram code configured to carry out the method of claim
 1. 26. A methodfor providing a diagnosis using a digital code in an optical coherencetomography imaging system, the method comprising: acquiringinterferometric cross-correlation data representative ofmultidimensional information unique to a sample, the multidimensionalinformation including one, two, or three spatial dimensions plus zero,one or two time dimensions; processing the multidimensionalinterferometric cross-correlation data into one or more images processedto represent one or more of time invariant information about the sample,slowly time variant information about the sample, or time variantstructural or functional information about the sample; and selecting asubset of the multidimensional data; reducing the selected subset ofdata to a two dimensional subset of data; performing one or more spatialor temporal frequency transforms of the two dimensional subsets toderive a unique representation of the sample; reducing the transforminto a unique digital code associated with the sample. comparing theunique digital code associated with the sample to a library of referencecodes, each of the reference codes in the library of reference codesbeing indicative of a class of objects; determining if the sampleassociated with the unique digital code falls into at least one of theclasses of objects associated with the reference codes based on a resultof the comparison; and formulating a diagnostic decision based on thewhether the unique digital code associated with the sample falls into atleast one of the classes associated with the reference code.
 27. Themethod of claim 26, wherein the optical coherence tomography systemcomprises: an optical source; an optical splitter configured to separatea reference optical signal from a sample optical signal; and an opticaldetector configured to detect an interferometric cross-correlationbetween the reference optical signal and the sample optical signal. 28.The method of claim 27, wherein the unique digital code comprises afirst unique digital code, the method further comprising: storing thedigital code; comparing the first unique digital code to a second uniquedigital code of the sample acquired from a second position within thesample, of the same sample acquired at a different time and/or of adifferent sample; and establishing a degree of equivalence between thefirst and second unique digital codes.
 29. The method of claim 26,wherein the method is performed using an optical coherence tomography(OCT) imaging system.
 30. A computer program of providing a digital codeassociated with an image, the computer program product comprisingcomputer program code embodied in a computer readable medium, thecomputer program code comprising program code configured to carry outthe method of claim
 26. 31. A method of providing a diagnosis using adigital code using a Fourier domain optical coherence tomography imagingsystem, the method comprising: acquiring frequency-dependentinterferometric cross-correlation data representative ofmultidimensional information unique to a sample, the multidimensionalinformation including zero or one frequency dimensions, one, two, orthree spatial dimensions and zero, one, or two time dimensions;processing the multidimensional interferometric cross-correlation datainto one or more images processed to represent one or more of timeinvariant information about the sample, slowly time variant informationabout the sample, or time variant structural or functional informationabout the sample; selecting a subset of the multidimensional data;reducing the subset of data to a two dimensional subset of data;performing one or more spatial or temporal frequency transforms of thetwo dimensional subsets to derive a unique representation of the sample;reducing the transform into a unique digital code that provides a uniquesignature of the multidimensional data; comparing the unique digitalcode associated with the sample to a library of reference codes, each ofthe reference codes in the library of reference codes being indicativeof a class of objects; determining if the sample associated with theunique digital code falls into at least one of the classes of objectsassociated with the reference codes based on a result of the comparison;and formulating a diagnostic decision based on the whether the uniquedigital code associated with the sample falls into at least one of theclasses associated with the reference code.
 32. The method of claim 31,wherein the Fourier domain optical coherence tomography imaging systemcomprises: an optical source; an optical splitter configured to separatea reference optical signal from a sample optical signal; and an opticaldetector configured to detect a frequency-dependent interferometriccross-correlation between the reference signal and the sample signal.33. The method of claim 32, wherein the unique digital code comprises afirst unique digital code, the method further comprising: storing theunique digital code; comparing the unique digital code to a secondunique digital code of the same sample acquired from a separate positionwithin the sample, of the sample acquired at a separate time, of adifferent sample; and establishing a degree of equivalence between thefirst and second unique digital codes.
 34. The method of claim 33,wherein processing of frequency-dependent interferometriccross-correlation data comprises obtaining a Fourier transformation ofthe frequency-dependent dimension to provide a spatial dimension. 35.The method of claim 31: wherein the image is a volumetric image of afingernail of a subject; wherein the volumetric image includes a seriesof one-dimensional lines that provide information on scattering from thefingernail and fingernail bed as a function of depth; wherein a seriesof lines optically contiguous are arrayed in a two-dimensional framethat represents a cross section of the fingernail perpendicular to anaxis of the finger, and wherein the method further comprises acquiring aseries of frames to produce a volume.
 36. The method of claim 35,further comprising: segmenting a nailbed from within the volumetricimage of the fingernail using an automated or manual segmentationtechnique; and averaging one or more frames in order to produce anaverage-valued image of multiple cross-sectional locations of thenailbed or to improve the signal-to-noise ratio of the nailbed imagealong one cross-section.
 37. The method of claim 36, further comprisingprocessing a digital code from the cross-sectional image of segmentednailbed region of at least one frame.
 38. The method of claim 37,wherein processing the multidimensional interferometriccross-correlation data comprises processing an intensity projection fromtwo or more frames of the segmented nailbed, the method furthercomprising processing a digital code from the intensity projection. 39.The method of claim 31, wherein the image is a volumetric image of aniris of an eye of a subject, wherein the volumetric image includes aseries of one-dimensional lines that provide information on scatteringfrom the iris as a function of depth, wherein a series of linesoptically contiguous are arrayed in a two-dimensional frame thatrepresents a cross section of the iris perpendicular to an axis of theeye, the method further comprising acquiring a series of frames toproduce a volume.
 40. The method of claim 39, further comprisingconstructing the volumetric image from a series of concentric circularscans approximately centered on a pupil of the eye.
 41. The method ofclaim 39, further comprising: segmenting one or more layers of the irisfrom within the volumetric image of the iris using an automated ormanual segmentation technique; and averaging one or more frames in orderto produce an average-valued image of multiple cross-sectional locationsof the iris or to improve the signal-to-noise ratio of the iris image atcross section.
 42. The method of claim 41, further comprising processinga digital code from the cross-sectional image of segmented iris regionof at least one frame.
 43. The method of claim 39, further comprising:processing an intensity projection from two or more frames of thesegmented iris; and processing a digital code from the intensityprojection.
 44. The method of claim 31, wherein the method is performedusing an optical coherence tomography (OCT) imaging system.
 45. Acomputer program of providing a digital code associated with an image,the computer program product comprising computer program code embodiedin a computer readable medium, the computer program code comprisingprogram code configured to carry out the method of claim
 31. 46. Anoptical coherence tomography imaging system comprising: means foracquiring interferometric cross-correlation data representative ofmultidimensional information unique to a sample, the multidimensionalinformation including one, two, or three spatial dimensions plus zero,one or two time dimensions; means for processing the multidimensionalinterferometric cross-correlation data into one or more images processedto represent one or more of time invariant information about the sample,slowly time variant information about the sample, or time variantstructural or functional information about the sample; and means forselecting a subset of the multidimensional data; means for reducing theselected subset of data to a two dimensional subset of data; means forperforming one or more spatial or temporal frequency transforms of thetwo dimensional subsets to derive a unique representation of the sample;means for reducing the transform into a unique digital code associatedwith the sample; means for comparing the unique digital code associatedwith the sample to a library of reference codes, each of the referencecodes in the library of reference codes being indicative of a class ofobjects; means for determining if the sample associated with the uniquedigital code falls into at least one of the classes of objectsassociated with the reference codes based on a result of the comparison;and means for formulating a diagnostic decision based on the whether theunique digital code associated with the sample falls into at least oneof the classes associated with the reference code.
 47. The system ofclaim 46, further comprising: an optical source; an optical splitterconfigured to separate a reference optical signal from a sample opticalsignal; and an optical detector configured to detect an interferometriccross-correlation between the reference optical signal and the sampleoptical signal.
 48. The system of claim 47, wherein the unique digitalcode comprises a first unique digital code, the system furthercomprising: means for storing the digital code; means for comparing thefirst unique digital code to a second unique digital code of the sampleacquired from a second position within the sample, of the same sampleacquired at a different time and/or of a different sample; and means forestablishing a degree of equivalence between the first and second uniquedigital codes.
 49. A Fourier domain optical coherence tomography imagingsystem comprising: means for acquiring frequency-dependentinterferometric cross-correlation data representative ofmultidimensional information unique to a sample, the multidimensionalinformation including zero or one frequency dimensions, one, two, orthree spatial dimensions and zero, one, or two time dimensions; meansfor processing the multidimensional interferometric cross-correlationdata into one or more images processed to represent one or more of timeinvariant information about the sample, slowly time variant informationabout the sample, or time variant structural or functional informationabout the sample; means for selecting a subset of the multidimensionaldata; means for reducing the subset of data to a two dimensional subsetof data; means for performing one or more spatial or temporal frequencytransforms of the two dimensional subsets to derive a uniquerepresentation of the sample; means for reducing the transform into aunique digital code that provides a unique signature of themultidimensional data; means for comparing the unique digital codeassociated with the sample to a library of reference codes, each of thereference codes in the library of reference codes being indicative of aclass of objects; means for determining if the sample associated withthe unique digital code falls into at least one of the classes ofobjects associated with the reference codes based on a result of thecomparison; and means for formulating a diagnostic decision based on thewhether the unique digital code associated with the sample falls into atleast one of the classes associated with the reference code.
 50. Thesystem of claim 49, further comprising: an optical source; an opticalsplitter configured to separate a reference optical signal from a sampleoptical signal; and an optical detector configured to detect afrequency-dependent interferometric cross-correlation between thereference signal and the sample signal.
 51. The system of claim 49,wherein the unique digital code comprises a first unique digital code,the system further comprising: means for storing the unique digitalcode; means for comparing the unique digital code to a second uniquedigital code of the same sample acquired from a separate position withinthe sample, of the sample acquired at a separate time, of a differentsample; and a means for establishing a degree of equivalence between thefirst and second unique digital codes.